
Contents
-
-
-
-
-
-
-
-
-
-
-
-
-
23.1 Introduction 23.1 Introduction
-
23.2 Finding good forecasting models 23.2 Finding good forecasting models
-
23.3 Prior specification then estimation 23.3 Prior specification then estimation
-
23.4 Conventional model selection 23.4 Conventional model selection
-
23.5 Model averaging 23.5 Model averaging
-
23.5.1 Collinearity in forecasting models 23.5.1 Collinearity in forecasting models
-
23.5.2 Changes in collinearity and model mis-specification 23.5.2 Changes in collinearity and model mis-specification
-
-
23.6 Factor models 23.6 Factor models
-
23.7 Selecting factors and variables jointly 23.7 Selecting factors and variables jointly
-
23.8 Using econometric models for forecasting 23.8 Using econometric models for forecasting
-
23.8.1 Cointegrated DGP 23.8.1 Cointegrated DGP
-
-
23.9 Robust forecasting devices 23.9 Robust forecasting devices
-
23.10 Using selected models for forecasting 23.10 Using selected models for forecasting
-
23.11 Some simulation findings 23.11 Some simulation findings
-
23.12 Public-service case study 23.12 Public-service case study
-
23.13 Improving data accuracy at the forecast origin 23.13 Improving data accuracy at the forecast origin
-
23.13.1 New sources of contemporaneous information 23.13.1 New sources of contemporaneous information
-
23.13.2 Changing database 23.13.2 Changing database
-
23.13.3 Nowcasts of Euro-area quarterly GDP 23.13.3 Nowcasts of Euro-area quarterly GDP
-
-
23.14 Conclusions 23.14 Conclusions
-
-
-
-
-
Cite
Abstract
Forecasting is different: the past is fixed, but the future is not. Practical forecasting methods rely on extrapolating presently available information into the future. No matter how good such methods are, they require that the future resembles the present in the relevant attributes. Intermittent unanticipated shifts violate that requirement, and breaks have so far eluded being predicted. If no location shifts ever occurred, then the most parsimonious, congruent, undominated model in-sample would tend to dominate out of sample as well. However, if data processes are wide-sense non-stationary, different considerations matter for formulating, selecting, or using a forecasting model. In practice, the robustness to location shifts of a model formulation can be essential for avoiding systematic forecast failure, which may entail selecting from a different class of models that need not even be congruent in-sample: complete success at locating the LDGP need not improve forecasting. However, by transforming a selected congruent parsimoniously encompassing model to a more robust form before it is used in forecasting, causal information can be retained while avoiding systematic forecast failure. The chapter also notes other ways of selecting forecasting models, including model averaging and factor approaches, but focuses on transformations of selected models of the LDGP.
Sign in
Personal account
- Sign in with email/username & password
- Get email alerts
- Save searches
- Purchase content
- Activate your purchase/trial code
- Add your ORCID iD
Purchase
Our books are available by subscription or purchase to libraries and institutions.
Purchasing informationMonth: | Total Views: |
---|---|
August 2024 | 1 |
Get help with access
Institutional access
Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:
IP based access
Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.
Sign in through your institution
Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.
If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.
Sign in with a library card
Enter your library card number to sign in. If you cannot sign in, please contact your librarian.
Society Members
Society member access to a journal is achieved in one of the following ways:
Sign in through society site
Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:
If you do not have a society account or have forgotten your username or password, please contact your society.
Sign in using a personal account
Some societies use Oxford Academic personal accounts to provide access to their members. See below.
Personal account
A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.
Some societies use Oxford Academic personal accounts to provide access to their members.
Viewing your signed in accounts
Click the account icon in the top right to:
Signed in but can't access content
Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.
Institutional account management
For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.